Data Science Blog

FEBRUARY 23, 2023

1, Data is the new oil, but labeled data might be closer to it Even though we have been in the 3rd AI boom and machine learning is showing concrete effectiveness at a commercial level, after the first two AI booms we are facing a problem: lack of labeled data or data themselves. That is, is giving supervision to adjust via.

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How to Learn Machine Learning

MARCH 25, 2025

It plays a crucial role in areas like customer segmentation, fraud detection, and predictive analytics. At the core of machine learning, two primary learning techniques drive these innovations. These are known as supervised learning and unsupervised learning.

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Dataconomy

MARCH 27, 2023

Definition and purpose of RPA Robotic process automation refers to the use of software robots to automate rule-based business processes. The goal is to create algorithms that can make predictions or decisions based on input data, without being explicitly programmed to do so.

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The MLOps Blog

MARCH 21, 2023

Your data scientists develop models on this component, which stores all parameters, feature definitions, artifacts, and other experiment-related information they care about for every experiment they run. Building a Machine Learning platform (Lemonade). Design Patterns in Machine Learning for MLOps (by Pier Paolo Ippolito).

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How to Learn Machine Learning

MAY 2, 2025

Azure ML supports various approaches to model creation: Automated ML : For beginners or those seeking quick results, Automated ML can generate optimized models based on your dataset and problem definition. Simply prepare your data, define your target variable, and let AutoML explore various algorithms and hyperparameters.

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Dataconomy

MARCH 27, 2023

Definition and purpose of RPA Robotic process automation refers to the use of software robots to automate rule-based business processes. The goal is to create algorithms that can make predictions or decisions based on input data, without being explicitly programmed to do so.

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Data Science 101

OCTOBER 31, 2019

A definition from the book ‘Data Mining: Practical Machine Learning Tools and Techniques’, written by, Ian Witten and Eibe Frank describes Data mining as follows: “ Data mining is the extraction of implicit, previously unknown, and potentially useful information from data. Classification. Regression.

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